Bibliographic record
Abstract
This minor release adds GenoFLU for H5 genotyping and HA cleavage site output with VADR annotations. This release also adds a script to classify HA cleavage sites based on mono-/multibasicity and low/high pathogenicity. Changes feat: GenoFLU v1.05 for H5 genotyping. feat: Added --custom_flu_minfo option to specify custom flu.minfo for VADR. The default flu.minfo is the same as the VADR flu v1.6.3-2 model except that it includes cleavage site info. Feature table, GenBank and GFF files should now have a misc_feature for HA cleavage site info. feat: bin/cleavage_site.py to classify HA cleavage sites. feat: Added VADR subtype prediction into subtyping report. VADR subtype predictions are pulled from the output .mdl files. feat: Added subtyping report output directory containing CSV for each sheet in the Excel report. fix: MultiQC converts the general info table into a violin plot if there are more than 500 rows in the table by default. Added max_table_rows: 1000000 to multiqc_config.yaml to avoid this conversion in most cases. What's Changed Add GenoFLU and HA cleavage site prediction by @cerdelyan in https://github.com/CFIA-NCFAD/nf-flu/pull/103 Release 3.7.0 by @peterk87 in https://github.com/CFIA-NCFAD/nf-flu/pull/104 New Contributors @cerdelyan made their first contribution in https://github.com/CFIA-NCFAD/nf-flu/pull/103 Full Changelog: https://github.com/CFIA-NCFAD/nf-flu/compare/3.6.2...3.7.0
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.226 | 0.373 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".